Using Neural Networks and OLAP Tools to Make Business Decisions

According to the Globe article, only top managers and policy-makers with a background in technology in state agencies as well as the staff of the Information Technology Division should have access to the specialized software.

The software, a so-called "business intelligence" program, would allow state officials to view dozens of state expenditures, caseloads, expenses, and other indicators at once, providing a high-level snapshot of all operations.

Appendix 1: Online Analytical Processing

Online Analytical Processing (OLAP) databases facilitate business-intelligence queries. OLAP is a database technology that has been optimized for querying and reporting, instead of processing transactions. The source data for OLAP is OLTP databases that are commonly stored in data warehouses. OLAP data is derived from this historical data, and aggregated into structures that permit sophisticated analysis. OLAP data is also organized hierarchically and stored in cubes instead of tables. It is a technology that uses multidimensional structures to provide rapid access to data for analysis. This organization makes it easy for a PivotTable report or PivotChart report to display high-level summaries, such as sales totals across an entire country or region, and also display the details for sites where sales are particularly strong or weak.

OLAP databases are designed to speed up the retrieval of data. Because the OLAP server, rather than Microsoft Office Excel, computes the summarized values, less data needs to be sent to Excel when you create or change a report. This approach enables you to work with much larger amounts of source data than you could if the data were organized in a traditional database, where Excel retrieves all of the individual records and then calculates the summarized values.

OLAP databases contain two basic types of data: measures, which are numeric data, the quantities and averages that you use to make informed business decisions, and dimensions, which are the categories that you use to organize these measures. OLAP databases help organize data by many levels of detail, using the same categories that you are familiar with to analyze the data.

Appendix 2: Types of Neural Networks

There are various types of neural networks, differing in structure, kinds of computations performed inside neurons, and training algorithms. One type offered in NeuralTools is the Multi-Layer Feedforward Network. With MLF nets, a NeuralTools user can specify whether there should be one or two layers of hidden neurons, and how many neurons the hidden layers should contain (NeuralTools provides help with making appropriate selections). NeuralTools also offers Generalized Regression Neural Nets and Probabilistic Neural Nets; these are closely related, with the former used for numeric prediction, and the latter for category prediction/classification. With GRN/PN nets, there is no need for the user to make decisions about the structure of a net. These nets always have two hidden layers of neurons, with one neuron per training case in the first hidden layer, and the size of the second layer determined by some facts about training data.

Figure 9: From the introduction of The Next Generation of Neural Networks, one of the videos in the References.

Appendix 3: Live Prediction

Live Prediction is a powerful capability of NeuralTools (Industrial version only) that allows you to perform predictions automatically in Excel without going through a specific Predict operation. With Live Prediction, NeuralTools places formulas in the cells where the predicted dependent variable values are shown. These formulas use a custom NeuralTools function to calculate the predicted values, such as:

The actual formula is added to your worksheet by NeuralTools; you do not need to enter it. The arguments let NeuralTools identify the trained network in use, along with the location of the independent values in the worksheet. When the input independent variable values for a case are added or changed, NeuralTools will automatically return a new predicted value. This makes it simple to add and generate predictions for new cases using an existing trained net.